Tag: Urban Dynamics Blog

Comparing Minneapolis wages to wages in North Minneapolis

TSS Admin

As Aristotle explored in his Metaphysics: Book Delta, the parts of something, say the parts of a city, are divisions of the whole that can be differentiated from one another by quantification or by qualification. In the sense of quantifying, North Minneapolis can be differentiated from Minneapolis by observational data, for example, unemployment rates, education rates, and wages.

In the sense of qualifying, North Minneapolis can be differentiated by recognition of area. But it should be noted that the geography of North Minneapolis is still the geography of Minneapolis. It is just a recognition of a specified area, which is not Northeast Minneapolis, South Minneapolis, or Southwest Minneapolis.

Furthermore, North Minneapolis is broken down further by quantification and qualification into area codes: 55411 and 55412. Thus, the 55411 and 55412 zip codes are distinguishable by name and specific geography, this is obvious, and by observational data.

For example, previous articles in this blog have shown the 55411 zip code to be the zip code with the highest number of reported crimes in North Minneapolis; whereas, previous articles in this blog have shown the 55412 zip code to be the zip code with the highest number of foreclosures over the past decade.

Graph 1

Utilizing this systemic approach, the wages between Minneapolis and North Minneapolis, specifically the 55411 zip code, can be differentiated and analyzed.

Thus, are the dynamics of the wages (how wages change over time) shown to be relatively equal to one another? Are the dynamics of the wages of the 55411 zip code shown to be greater than Minneapolis? Or are the dynamics of the wages of the 55411 zip code shown to be less than Minneapolis?

As Graph 1 illustrates, we can see that the wage rate of Minneapolis is steeper than the wage rate of the 55411 zip code in Graph 2. And we’re not just eyeing this. We can see this distinctly via the linearization equations in Graph 1 and Graph 2.

The linearization equation in Graph 1 (y = 6.4152x + 1083.1) shows a rate of 6.4 and the linearization equation in Graph 2 (y = 2.2805x + 823.6) shows a rate of 2.3, if both rates of change are rounded-off. Obviously, 6.4 is greater than 2.3, and by quite a bit. Why is this important?

Graph 2

Dynamically (how wages change over time), this shows the wages of Minneapolis are growing at a greater rate than the wages of the 55411 zip code. Of course, these equations also show that the average weekly wages of Minneapolis are between $250 and $300 higher than the 55411 zip code.

This little bit of information ought to provide policy makers with some much-needed direction to create and apply economic policy. Of course the operative modal verb is “ought to.”

So do you think local policy makers would consider differentiating between the part and the whole when creating economic policy? Or do you think local policy makers would just create and apply the same policy for both the part and the whole?

 

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Photo credit: Wikimedia Commons

 

 

 

 

 

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Chicagoland: 2017 Homicide totals so far

TSS Admin

Nothing brings visitors and views to mainstream media websites quite like homicides in the windy city. When homicides are up, mainstream visitor traffic is good.

But don’t expect to hear from the mainstream media anytime soon. This is because homicides are down from last year at this time. Yes! It’s true.

Normal Distribution, Wikimedia Commons

According to data compiled by the Chicago Tribune, there were 260 homicides through the month of May in 2016. That was up significantly from 2015. In contrast, there have been 235 homicides so far this year. For those keeping count, that’s a reduction of 9.6 percent from last year.

Hopefully, Memorial Day weekend will stay relatively quiet this year and homicides will remain at 235.

Of course, the warm summer months are always the busiest time for crime in general, including homicides. Historically this has been the trend, and this is exactly what the data sets are saying.

If the trends hold, then homicides in Chicago should follow a normal distribution, i.e., a bell-curve, although the 2016 distribution of homicides skewed left.

Skewed-left distribution, University of Florida

This means that roughly about 68 percent of the homicides should happen within the warm months of the summer, or one standard deviation from the mean as the normal distribution above illustrates.

Moreover, about 32 percent of the homicides should occur outside of the one standard deviation, or outside the warmer months.

Does this mean the warm months of Chicago in 2017 will see more homicides than the warm months of Chicago in 2016? It does not.

So far, homicides are down from 2016 and if this trend continues throughout the summer months, then homicides should remain down. But the reader should keep in mind that homicides are very difficult to predict.

The only reason it is being suggested that homicides may trend below last year is because homicides are down. If they were up, then the prediction would be the opposite. Of course, this method is an archaic form of bayesian statistics, so take it with a grain of salt.

What do you think? Do you think homicides will remain lower than last year? Or do you think homicides will explode over the summer months? Either way, please provide your reasons and explanations below.

 

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Want the economy to grow? It’s time to look at cities and efficiency

The economy is a hot topic in the presidential debates and is among the top public concerns. But the “economy” is a loose and hazy notion and, for politicians, a convenient place to make promises. The Conversation

Even the solutions are pitched at a high level of abstraction. On the Republican side, the common answer is to reduce taxes, which also has the obvious attraction of aiding their donor class, and to cut back on government regulations. On the Democratic side, one response is to increase taxes on the wealthy, with the precise causal mechanism never explained or demonstrated.

The reality is rather more daunting and the answers could lie in place few politicians discuss explicity: cities.

Urban economics

There are of course real economic concerns of low growth, stagnant incomes and rising inequality.

Given that growth projections are limited, we need to be thinking more about productivity gains. That means we need to make our economy more efficient – generating more economic value with the same inputs. And one way to do this is to improve the productivity of our cities in various ways, including better land use, beefed-up infrastructure and smarter technology.

Metro areas in the U.S. now house 83 percent of the population and are the main site for innovation and job growth. The 100 largest metro areas hold 69 percent of all jobs and are responsible for three-quarters of the nation’s GDP.

The bigger, the more productive: Larger, denser cities are more efficient economically. OECD

If we are serious about growing our economy, then getting our cities to work better is just as important as tax reform or wage policy. The problem is that cities tend to be discussed in terms of redistributional issues, such as welfare or race relations, but rarely as a platform for addressing the “economy.”

Consider just some of the traditional inputs of land, labor and energy. Cities use enormous amounts of energy. So policies about urban energy use and urban transportation are not just urban concerns, they are matters of national economic concern.

In other countries, there is a closer connection in political discourse between the economy and the city. In Australia, where close to half of the population lives in the five largest cities, the idea of improving living standards and competitiveness by increasing urban productivity is now part of political discussions.

Traditional economics is not much help, as productivity is generally used with reference to individual firms or workers. Rarely is it used to measure the productivity of cities. Even when they do look at cities, economic theorists rarely move on from noting that large cities achieve agglomeration economies through the clustering of activities, labor pooling and knowledge spillovers.

This explains an economic rationale for cities but does not help us make cities more productive. How can we do that?

Bigger, denser, more productive

The good news is that more people are looking at this issue with more case studies that look at how productivity is related to educational levels and labor markets.

It turns out that we should be encouraging cities to become bigger and more dense if we want to improve economic performance.

Consider transport. There are significant cost savings in increasing the ridership of mass transit systems compared with constructing expensive new systems. Even small-scale policy changes have rolling consequences. Improving traffic light sequencing, for example, reduces travel times, emissions, fuel consumption and road accidents.

Buses don’t normally figure in talk of the economy, but a city’s transportation system can make a city as a whole – and thus the economy – more productive. lodekka/flickr, CC BY

Meanwhile, encouraging telecommuting, while reducing the benefits of face-to-face contact in real time, generates savings in terms of time and energy costs as well as the wear and tear on commuters slogging their way through traffic. The collective gain is a more efficient city and greater economic productivity.

Also, a single government authority in a large city is more efficient than a multiplicity of municipal governments. One study of cities across five countries found that a metro region with many municipal governments, has, on average, six percent lower productivity than a city with one metropolitan authority.

Cities are a target-rich environment for improving productivity because they are places where public policies have leverage. Dysfunction at the federal level, likely to halt any ambitious proposals discussed in the presidential elections, does not stop experiments at the city level. And here a combination of nonpartisan federal and local policies can achieve savings.

For example, new federal legislation has allowed companies to provide the same level of benefits for mass transit users and carpoolers as it did for parkers. Against this background, city authorities can enable more carpooling by setting aside designated spots for informal carpools.

Improving urban efficiencies has the added benefit of improving sustainability and helping deal with climate change.

Social issues and big urban data

Productivity has a cold-blooded sound to it, as if citizens are imagined just as labor inputs to be trained and moved around to increase efficiencies. But there is a meshing of economic and social concerns.

A more efficient land use and transportation system, for example, means people spend less time and money commuting. I was reminded of this when seeing the route map of a low-income worker in Atlanta, Georgia, whose two-hour journey to work involves 118 bus stops and a nine-minute train ride.

Can technology make a difference? We now have lots of data on the flows of energy, people, goods, capital and ideas. While big data on its own does not provide the solution, the intelligent use of these data can provide us with a real-time handle on urban productivity to provide benchmarks of performance and measures of progress. And once urban productivity is measured, it can be improved.

Big data could also help improve our infrastructure, which would aid productivity and reduce economic losses. Many bridges need renovation and replacement. But if we use good-quality data on how much repair they need as well as how much traffic they support, we would be in a better position to prioritize our infrastructure funds so that the most dangerous and the most frequented were targeted first.

We are still at a very early stage of using big urban data to provide smarter, safer, more efficient and more socially just cities. An important start is that we realize that more of our economic activity takes place in cities and improving urban economic performance is the road to economic growth and social justice.

John Rennie Short, Professor, School of Public Policy, University of Maryland, Baltimore County

 

Photo credit: Gregor Smith/flickr, CC BY

Photo explanation: Traffic jams in cities, such as this one in Atlanta, have economic costs, including lower productivity.

 

 

 

This article was originally published on The Conversation. Read the original article.

Has the number of business establishments in Minneapolis increased since 2006?

Analyzing data always provides interesting insights. For example, a simple analysis of establishment (business) data from the Minnesota Department of Employment and Economic Development (DEED) reveals some fascinating insights into the systems dynamics – a system changing over time – of the Minneapolis marketplace with respect to business firms.

As the data, Graph 1, reveals, the number of establishments, or businesses, in Minneapolis has been decreasing for at least the past 10 years. Why is this so? This blog will not venture into such speculation. This is because the system’s perspective is limited to only establishment data. A multivariate perspective (multiple perspectives) is needed to find such possible reasons.

 

Graph 1

As Graph 1 illustrates, the number of firms per quarter has been decreasing since at least 2006. And although this rate has been variable, which is to be expected because the marketplace is probabilistic, the overall trend has been negative.

Furthermore, this overall negative trend can be shown in a couple of different ways. First, it can be illustrated via linearization. As Graph 2 shows, the overall trend is negative. That is, the Minneapolis marketplace decreased in the total number of establishments between the 1st Quarter of 2006 and the 3rd Quarter of 2016.

Graph 2

It should be noted that the linearization seen here is not the same linearization as in dynamical systems. In dynamical systems, linearization is an approximation “to a function at a given point.” Obviously this is not the case here.

Again, the main idea to take away from linearization, in the way it is used here, is the overall trend of the graph – did the marketplace gain businesses over the period stated in Graph 2, did the marketplace lose businesses over the period stated in Graph 2, or did the marketplace remain about the same over the period stated in Graph 2?

And finally, the marketplace behavior of business establishments in Minneapolis can be illustrated through Vector Algebra. Yes! That’s right – Vector Algebra. In this case, there will be no math included, just an illustration of direction via Graph 3, so there is no reason to be alarmed.

Graph 3

As Graph 3 shows, the overall dynamics, or vector, of the marketplace is negative in regards to the number of establishments from the 1st Quarter of 2006 through the 3rd Quarter of 2016. And the vectors, those letter “a’s” with the hats over them, further illustrate a greater decrease in total establishment between the 1st Quarter of 2006 and the 3rd Quarter of 2010 than between the 3rd Quarter of 2010 and the 3rd Quarter of 2016.

Of course, these vectors could further be broken into smaller vectors. But the way the algebra works, each vector that is computed in this system should add up to the overall vector, which is negative. Thus, this decomposition of the system behavior provides a more conclusive way of viewing the dynamics of this particular system than how linearization is being used here. And the vector idea, along with the math, supports the initial observation. That is, the total number of establishments in the Minneapolis marketplace has decreased since at least the 1st Quarter of 2006.

So how does this market behavior compare to the county or state level? How does Minneapolis compare to the zip codes that reside within it?

And another interesting question to ask one’s self is, has employment increased, decreased, or stayed the same in Minneapolis? And what does this mean for the number of employees per establishment?

 

Matt Johnson is a writer for the Urban Dynamics blog; and is a mathematical scientist. He has also contributed to the Iowa State Daily and Our Black News.

You can connect with him directly in the comments section, and follow him on LinkedIn or Facebook

Photo credit: The Systems Scientist

 

 

 

 

 

Copyright ©2017 – The Systems Scientist

 

Minnesota: Making distinctions between labor forces in the state system

By Matt Johnson

Diagram 1

Making distinctions between different levels of a system is an important first step to thinking about systems in a systematic way. But how can this be accomplished?

This can be accomplished by utilizing Diagram 1 as a visual aide. As Diagram 1 illustrates, the United States is the primary system, or general system, whereas the Region, Division, State, County, City, Zip Code, Census Track, and Block Group are all sub-systems of the United States.

And in this blog, distinctions will be made between the labor forces in the Minnesota system, the Hennepin County System, and Minneapolis system. Making these distinctions will help partition out where these respective systems reside in the grand scheme of things, and how their respective labor forces differentiate from each other. But first, two terms will be defined: labor force and system.

What is a labor force?

According to the Bureau of Labor Statistics, a labor force is a population of workers who are either working in the marketplace or who are actively looking for work in the marketplace.

Indeed, we should note a labor force does not account for those persons not participating in the marketplace. The point here is we will be looking at those citizens who are actively engaged in the marketplace via the Minnesota labor force, the Hennepin County labor force, and the Minneapolis labor force.

What is a system?

The simplest definition contains three parts, or three conditions: a system contains elements, these elements interact, and a function is produced from this interaction. These elements could be a small group of elements or a large group of elements. Of course how elements exist in the system is either observable or unobservable (we will not address the unobservable or uncountable in this blog).

This means a person could observe nine baseball players in dark-blue jerseys on a baseball diamond. These baseball players would then be the elements of the system. Furthermore, these nine baseball players in dark-blue jerseys would be interacting with each other, while out in the field or while hitting, throughout the nine innings of the game. And the interactions in this small system would produce an outcome for the baseball team in dark-blue jerseys (possible outcomes produced would be a win or a loss).

For purposes of this blog, we will assume these three conditions are satisfied.

The Labor Force

To recall, we will focus on three levels of the nine-level system presented in Diagram 1: state, county, and city. Before proceeding, we should note that the systems levels of metro area, district/ward, and neighborhood were not included in Diagram 1 for brevity (those levels of the system will be examined in future blogs).

First, and moving forward, what kind of systems behavior should we see in the state labor force? That is, should we see positive, negative, or no growth since 2006?

Graph 1

As we can see, the labor force of Minnesota has been trending upwards since at least the 1st Quarter of 2006. Indeed, we also see that the market has fluctuated quite a few times, but it’s important that we understand that this fluctuation is normal behavior for a stochastic (probabilistic) system such as a labor force. So when we say the labor force of Minnesota has been trending upwards since at least the 1st Quarter of 2006, we are saying the overall behavior of the system has been positive.

Second, what kind of systems behavior should we see in the county labor force? That is, should we see positive, negative, or no growth since 2006?

Graph 2

Much like the Minnesota labor force, we can see in Graph 2 that the Hennepin County labor force has been trending upwards since 2006 as well. Sure! It to has fluctuated throughout, but again, that’s to be expected in a probabilistic system such as a marketplace.

Third, what kind of systems behavior should we see in the city labor force? That is, should we see positive, negative, or no growth since 2006?

Graph 3

In the observations of the three levels of the Minnesota system, we see that the Minneapolis labor force has been trending upwards since 2006 as well. Again, we observe peaks and valleys in the data, but the overall behavior has been positive. Thus we have seen positive growth over a ten-year period at the state, county, and city levels of the system, and making these distinctions has enlightened us by delving a bit deeper into the economic system of Minnesota.

Here are some questions we might want to ask ourselves. Would we continue to see this positive labor force growth over the past 10 years if we examined various zip codes in Minneapolis? By making distinctions and partitioning out say the 55411 and the 5549, would we see similar growth in both zip codes, for example? Would we see this same positive behavior if we examined various Minneapolis neighborhoods like Seward, Fulton, or Jordan, or would we see differences? And finally, would we see this same positive behavior if we examined various areas – a census track or block group – located inside various Minneapolis neighborhoods?

 

Matt Johnson is a writer for the Urban Dynamics blog; and is a mathematical scientist. He has also contributed to the Iowa State Daily and Our Black News.

You can connect with him directly in the comments section, and follow him on Facebook

Photo credit: Pixabay

 

 

 

 

 

Copyright ©2017 – The Systems Scientist

A quick view of an economic system

By Matt Johnson

In this short blog, I will illustrate one way an urban dynamicist, i.e., systems scientist, looks at an economic system and its data.

Diagram 1

Diagram 1 is hierarchical, derives from the U.S. Census Bureau, and represents a few of the many levels of an economic system. Moreover, each level of the economic system in Diagram 1 is further a sub-system, or sub-economy, of the general United States economy.

This means that a zip code, for example, can be examined as an economic system, and then it can be compared and contrasted with a city’s economic system. And this examination will illustrate similarities and differences between a sub-system, a zip code, and a general system, a city, for instance.

Thus, an urban dynamicist can partition out each level of the economic system and analyze each level as a distinct entity, although one system is still a sub-system of the one superior to it in the hierarchy. Within each level, differences, relationships, perspectives, dynamics, and models can be examined through data.

As stated before, each level of the system can be analyzed against the other levels of the system through data, because data provides a picture at each level of the system. For example, the State can be illustrated and compared to the Division, Zip Code, or Census Tract via crime densities, demographic comparisons and migration patterns, and economic variables such as median household incomes, unemployment rates, the labor force and labor participation rates.

Here is the stochastic (probabilistic) behavior of the labor force in Minneapolis over the past 10 years as seen here in Graph 1.

Graph 1

And here is the stochastic (probabilistic) behavior of the Minnesota labor force over the past 10 years as illustrated in Graph 2.

Future articles will delve deeper into the specifics of the behavior and dynamics of these two systems and their respective data sets. For now, the main point is that data can provide a picture of the economic systems at their respective levels of the system.

One last thought, Diagram 1 does not illustrate the interactions or dynamics that take place within each level of the system by itself, nor does it account for a lot of things. This is why the data is needed. So assumptions and conclusions should be limited.

As this focus on data continues, I will be utilizing the hierarchical model and other systems models to help illustrate and explain how economic systems can be better understood. In addition, I will be using systems theory along with applied mathematics to explore the complexity of systems. But I will also be working diligently and meticulously to convey this information to you the best I can.

As I get better at explaining this stuff to you, I hope your knowledge of systems, mathematics, and economics increases as well.

 

Matt Johnson is a writer for the Urban Dynamics blog; and is a mathematical scientist. He has also contributed to the Iowa State Daily and Our Black News.

You can connect with him directly in the comments section, and follow him on Facebook

Photo credit: Pixabay

 

 

 

 

 

Copyright ©2017 – The Systems Scientist

 

 

Chicagoland: 2017 homicide rate on track to match 2016 homicide rate

By Matt Johnson

There doesn’t seem to be an end in sight for the high number of homicides happening in Chicagoland. The midwestern city is on track to match its 2016 homicide total.

At the end of February 2016, Chicago had experienced 103 homicides. That was an increase of more than 96 percent from the year before. Matter of fact, there were a total of 52 homicides in January and February of 2015. In contrast, both 2016 and 2017 doubled 2015 numbers two years in a row.

In 2017, there were 55 homicides in January and 48 homicides in February according to the Chicago Tribune. Comparing 2017 to 2016, January saw a 3.6 percent decrease, which appeared promising. However, February made up for the decrease in homicides with a 6.7 percent increase. This bump in an otherwise traditionally quiet month for adverse socio-economic factors pushed Chicago back into the direction it desperately didn’t need to go.

2016-chicago-homicides-dwm

In addition, it should be noted that the majority of these homicides are concentrated in the same few neighborhoods year after year. Thus, homicides along with other adverse socio-economic factors are not an acute issue. They are chronic and the science and mathematics are clear on this point.

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In 2016, 4 of the 5 neighborhoods with the highest numbers of homicides were located on Chicago’s West Side.

top-5-homicide-neighborhoods-of-2016-dwm

And now in 2017, the West Side neighborhoods of Austin, Englewood, and Garfield Park are the top 3 deadliest neighborhoods in Chicago so far this year, and one ought to expect this unfortunate reality to continue because of historical data and trends. Again, there are adverse socio-economic factors that have not been addressed. 

As of this moment, and although these numbers could change in the next 24 hours, Austin has experienced 14 homicides, Garfield Park has experienced 10 homicides, and Englewood has experienced 8 homicides according to heyjackass.com (again, they provide reliable statistics and sources). North Lawndale has had 5 homicides so far this year.

If this homicide rate continues for the remainder of the year, then it is likely that Chicago will see another 785 to 800 homicides this year.

 

Matt Johnson is a writer for The Systems Scientist and the Urban Dynamics blog . He has also contributed to the Iowa State Daily and Our Black News.

He has a Bachelor of Science, Systems Science with focuses in applied mathematics and economic systems; and he is a member of the Society for Industrial and Applied Mathematics, and the International Society for the Systems Sciences.

You can connect with him directly in the comments section, and follow him on Twitter or on Facebook

You can also follow The Systems Scientist on Twitter or Facebook.

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Copyright ©2017 – The Systems Scientist